The superior quality provided by digital technologies has created a huge demand for digital products in general. Part of this digital revolution is the increased popularity of digital images. It is now possible to use a digital camera to capture an image and reproduce the image on some sort of display media using a personal computer (PC) monitor or high resolution printer. It has even become common practice to incorporate digital images into "web pages" available over a network such as the Internet and World Wide Web, or to send a digital image to another PC via electronic mail.
At the heart of this digital image revolution are image processing systems. These systems process the captured digital image to enhance the clarity and details of the image using sophisticated image processing algorithms. The use of these algorithms result in images that are substantially more accurate and detailed than previously achieved using older analog methods.
There remains, however, a substantial difference between how an image is perceived by a person and an image captured and reproduced on a display medium. Despite the improvements gained by digital image processing systems, these systems are still incapable of reproducing an image with the same level of detail, color constancy, and lightness of an actual scene as the eye, brain and nervous system of a human. This is due in part because the human nervous system has a greater dynamic range compression than is available on current digital systems. Dynamic range compression refers to the ability to distinguish varying levels of light. The human eye has a dynamic range compression of approximately 1000:1, which means that the human eye can distinguish approximately 1000 levels of light variations. By way of contrast, digital image systems typically use only eight bits per pixel which allows for a dynamic range compression of only 255:1. As a result, a digital image reproduced as a photograph would have far less detail in the darker and brighter regions of the photograph as compared to the actual scene perceived by a viewer.
Many techniques have been developed to compensate for this lighting deficiency. These techniques can be separated into two broad categories: (1) power law or non-linear techniques ("non-linear techniques"); and (2) retinex techniques. Each have their respective limitations.
Non-linear techniques use a non-linear relationship to expand one portion of the dynamic range while compressing another. These techniques generally enhance details in the darker regions at the expense of detail in the brighter regions. For example, each pixel of a digital image is represented using eight bits, and therefore is assigned a luminance value somewhere in the range of 0 to 255, with 0 representing the lowest light level and 255 the highest. If a region is comprised of pixels having low light values, detail is lost since there is very little gradient between the light values for each pixel. For example, it is difficult for a viewer to look at a picture and distinguish between a pixel having a luminance value of 23 and a pixel having a luminance value of 25. To solve this problem, conventional image processing system utilize non-linear techniques to increase the luminance values for neighboring pixels, thereby creating a greater degree of contrast between the pixels. Thus, the pixel having the luminance value of 25 might be assigned a higher value such as 28 to create a greater contrast between it and the pixel having a value of 23.
One problem associated with these non-linear systems is that they provide greater distinctions between pixels regardless of what lightness value a pixel may have been originally assigned. This results in the brighter areas which already have a "washed-out" appearance to become even more washed-out. Although these techniques result in greater detail in the darker regions, they do so at the expense of the brighter areas of the digital image. Further, these methods suffer from contours and abrupt boundaries.
Retinex techniques are based on the work of Dr. Edwin H. Land. The fundamentals of Mr. Land's retinex techniques as well as several variations are described in a paper by A. Moore titled "Spatial Filtering in Tone Reproduction and Vision," PhD Thesis, California Institute of Technology, Pasadena, Calif., 1992, and D. J. Jobson, et al. in a paper titled "A Multi-Scale Retinex for Bridging the Gap Between Color Images and the Human Observation of Scenes," IEEE Transactions on Image Processing: Special Issue on Color Processing, July 1997. In essence, these techniques increase or decrease the luminance value for a pixel based on the luminance values of surrounding pixels. These techniques are particularly useful for enhancing boundaries between lighter and darker regions of an image. These techniques, however, are unsatisfactory for a number of reasons. For example, the Moore reference describes a technique that grays out large uniform zones in the image. With respect to the Jobson reference, the described technique allows a shift in color in some images and is computational intensive.
In view of the foregoing, it can be appreciated that a substantial need exists for an image processing method and apparatus that solves the above-discussed problems.